System and method for energy plant optimization using mixed integer-linear programming

1. A method for optimizing operational settings for a plurality of energy devices, comprising:

representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters;

constraining the decision variables and operational parameters based on operational conditions and interrelationship between the plurality of energy devices;

generating a two-tiered model of the plurality of energy devices wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of some of the plurality of energy devices and their interaction; and

optimizing the two-tiered model to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices,

performing each of the above steps within a computer system.

2. The method of claim 1 , wherein the plurality of energy devices includes a sub-grouping of chiller units, a sub-grouping of heater units and an air handling device, each with a corresponding sub-model within the two-tiered model of the plurality of energy devices.

3. The method of claim 2 , wherein the sub-grouping of heater units includes one or more boilers and a feedwater tank.

4. The method of claim 2 , wherein the sub-grouping of cooling units includes one or more centrifugal chillers and a cooling tower.

5. The method of claim 1 , wherein optimizing the two-tiered model is performed using mixed integer-linear programming.

6. The method of claim 1 , wherein the step of optimizing the two-tiered model includes optimizing the top tier model and optimizing the bottom tier model iteratively.

7. The method of claim 1 , wherein the step of generating the two-tiered model includes representing the plurality of energy devices as a graph having a plurality of nodes that represent the sub-models of energy devices, utility sources, or ambient conditions and a plurality of edges that interconnect the nodes, each edge setting forth an operational relationship between the sub-models connected by the corresponding edges.

8. The method of claim 1 , wherein with respect to the top tier of the two-tiered model, each sub-model is treated as a gray box model and with respect to the bottom tier of the two-tiered model, each sub-model includes a detailed representation of the energy devices of that sub-model.

9. The method of claim 1 , wherein the step of optimizing the two-tiered model is performed using an optimization solver based on mathematical programming.

10. The method of claim 1 , wherein optimization of the two-tiered model provides a schedule of operation for the plurality of energy devices, dictating activation times, deactivation times or operational levels of the plurality of energy devices.

11. The method of claim 1 , wherein optimization of the two-tiered model provides real-time control of the plurality of energy devices, controlling activation times, deactivation times or operational levels of the plurality of energy devices.

12. The method of claim 1 , wherein the plurality of energy devices includes devices that utilize energy, devices that generate energy, or devices that store energy.

13. A computer system comprising:

a processor; and

a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for optimizing operational settings for a plurality of energy devices, the method comprising:

generating a model of the plurality of energy devices representing interaction of the energy devices with each other and operational variables; and

optimizing the model to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices,

implementing the model as a two-tiered model in which the energy devices are organized into sub-groupings and interaction between each of the sub-groupings is represented at a top tier of the model and interaction between each of the energy devices within each sub-grouping are represented at a bottom tier of the model.

14. The computer system of claim 13 , wherein each of the plurality of energy devices are represented in the model in terms of a set of decision variables and operational parameters.

15. The computer system of claim 13 , wherein optimization of the model is performed using mixed integer-linear programming.

16. A computer system comprising:

a processor; and

a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for optimizing operational settings for a plurality of energy devices, the method comprising:

representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters;

constraining the decision variables and operational parameters based on operational conditions and interrelationship between the plurality of energy devices;

generating a two-tiered model of the plurality of energy devices wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of some of the plurality of energy devices and their interaction;

optimizing the two-tiered model to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices; and implementing an optimization solver employing mixed integer-linear programming.

17. The computer system of claim 16 , wherein generating the two-tiered model includes representing the plurality of energy devices as a graph having a plurality of nodes that represent the sub-models of energy devices, utility sources, or ambient conditions and a plurality of edges that interconnect the nodes, each edge setting forth an operational relationship between the sub-models connected by the corresponding edges.

18. The computer system of claim 16 , wherein with respect to the top tier of the two-tiered model, each sub-model is treated as a gray box model and with respect to the bottom tier of the two-tiered model, each sub-model includes a detailed representation of the energy devices of that sub-model.